OPT OpenIR  > 光谱成像技术研究室
Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach
Liu, Li1,2; Shao, Ling1,2; Li, Xuelong3; Lu, Ke4,5; Shao, L
作者部门光学影像学习与分析中心
2016
发表期刊IEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
卷号46期号:1页码:158-170
产权排序3
摘要Extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. In this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. This is achieved via an evolutionary method, i.e., genetic programming (GP), which evolves the motion feature descriptor on a population of primitive 3D operators (e.g., 3D-Gabor and wavelet). In this way, the scale and shift invariant features can be effectively extracted from both color and optical flow sequences. We intend to learn data adaptive descriptors for different datasets with multiple layers, which makes fully use of the knowledge to mimic the physical structure of the human visual cortex for action recognition and simultaneously reduce the GP searching space to effectively accelerate the convergence of optimal solutions. In our evolutionary architecture, the average cross-validation classification error, which is calculated by an support-vector-machine classifier on the training set, is adopted as the evaluation criterion for the GP fitness function. After the entire evolution procedure finishes, the best-so-far solution selected by GP is regarded as the (near-) optimal action descriptor obtained. The GP-evolving feature extraction method is evaluated on four popular action datasets, namely KTH, HMDB51, UCF YouTube, and Hollywood2. Experimental results show that our method significantly outperforms other types of features, either hand-designed or machine-learned.
文章类型Article
关键词Action Recognition Feature Extraction Feature Learning Genetic Programming (Gp) Spatio-temporal Descriptors
学科领域Computer Science, Artificial Intelligence
WOS标题词Science & Technology ; Technology
DOI10.1109/TCYB.2015.2399172
收录类别SCI ; EI
关键词[WOS]PARTICLE SWARM OPTIMIZATION ; FEATURE-SELECTION ; CLASSIFICATION ; FEATURES ; INTERPOLATION ; ALGORITHM ; FRAMEWORK
语种英语
WOS研究方向Computer Science
项目资助者Chinese Academy of Sciences(KGZD-EW-T03) ; National Natural Science Foundation of China(61125106)
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号WOS:000367144300015
引用统计
被引频次:139[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/27736
专题光谱成像技术研究室
通讯作者Shao, L
作者单位1.Nanjing Univ Informat Sci & Technol, Coll Elect & Informat Engn, Nanjing 210044, Jiangsu, Peoples R China
2.Northumbria Univ, Dept Comp Sci & Digital Technol, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England
3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Beijing Ctr Math & Informat Interdisciplinary Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714
Liu, Li,Shao, Ling,Li, Xuelong,et al. Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach[J]. IEEE TRANSACTIONS ON CYBERNETICS,2016,46(1):158-170.
APA Liu, Li,Shao, Ling,Li, Xuelong,Lu, Ke,&Shao, L.(2016).Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach.IEEE TRANSACTIONS ON CYBERNETICS,46(1),158-170.
MLA Liu, Li,et al."Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach".IEEE TRANSACTIONS ON CYBERNETICS 46.1(2016):158-170.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
Learning Spatio-Temp(2314KB)期刊论文作者接受稿限制开放CC BY-NC-SA请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Liu, Li]的文章
[Shao, Ling]的文章
[Li, Xuelong]的文章
百度学术
百度学术中相似的文章
[Liu, Li]的文章
[Shao, Ling]的文章
[Li, Xuelong]的文章
必应学术
必应学术中相似的文章
[Liu, Li]的文章
[Shao, Ling]的文章
[Li, Xuelong]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。